ArticleScientific reports2022
Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A hybrid experimental and machine learning framework for designing and predicting compressive strength of ultra-high-performance concrete.Scientific reports · 2026Article
- Evaluating the predictive accuracy of supervised machine learning models to explore the mechanical strength of blast furnace slag incorporated concrete.Scientific reports · 2026Article
- A Comprehensive Review of Numerical and Machine Learning Approaches for Predicting Concrete Properties: From Fresh to Long-Term.Materials (Basel, Switzerland) · 2025Review
- Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.Scientific reports · 2025Article
- Predicting workability and mechanical properties of bentonite plastic concrete using hybrid ensemble learning.Scientific reports · 2025Article
- Presenting a model for estimating the cube compressive strength of self-compacting concrete in cast in-situ piles using GEP.Scientific reports · 2024Article
- Predicting the Compressive Strength of Sustainable Portland Cement-Fly Ash Mortar Using Explainable Boosting Machine Learning Techniques.Materials (Basel, Switzerland) · 2024Article
- Robust Machine Learning Framework for Modeling the Compressive Strength of SFRC: Database Compilation, Predictive Analysis, and Empirical Verification.Materials (Basel, Switzerland) · 2023Article
- Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams.Scientific reports · 2023Article
- Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar.Materials (Basel, Switzerland) · 2023Article
- Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
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Abstract
Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using machine learning and deep learning models. In this research, compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement is predicted using boosting machine learning (BML) algorithms, namely, Light Gradient Boosting Machine, CatBoost Regressor, Gradient Boosting Regressor (GBR), Adaboost Regressor, and Extreme Gradient Boosting. In these studies, the BML model's performance is evaluated based on prediction accuracy and prediction error rates, i.e., R
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